Papers by Lyuhao Chen
OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)
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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan
| Challenge: | Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets. |
| Approach: | They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models. |
| Outcome: | The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work. |
Table-R1: Inference-Time Scaling for Table Reasoning Tasks (2025.emnlp-main)
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| Challenge: | In this study, we explore inference-time scaling on table reasoning tasks. |
| Approach: | They propose a large-scale dataset of reasoning traces and a reinforcement learning with verifiable rewards approach to enable inference-time scaling on table reasoning tasks. |
| Outcome: | The proposed model matches or exceeds GPT-4.1 and DeepSeek-R1 models on diverse table reasoning tasks. |
Sentipolis: Emotion-Aware Agents for Social Simulations (2026.findings-acl)
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| Challenge: | Recent advances in reasoning and long-context memory are making large language models (LLMs) appear increasingly human-like, which has led researchers to adopt LLM agents as a substrate for social simulation. |
| Approach: | They propose a framework for emotionally stateful agents that integrates continuous Pleasure-Arousal-Dominance representation, dual-speed emotion dynamics, and emotion–memory coupling. |
| Outcome: | The proposed framework improves emotional grounded behavior, boosting communication, and emotional continuity across thousands of interactions over multiple base models and evaluators. |
Revisiting Automated Evaluation for Long-form Table Question Answering (2024.emnlp-main)
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| Challenge: | Existing automated metrics for long-form table question answering (LFTQA) are poorly correlated with human judgments and fail to distinguish between factually accurate responses and those that are factual incorrect. |
| Approach: | They propose to use a meta-evaluation dataset to assess the effectiveness of LLM-based LFTQA systems. |
| Outcome: | The proposed meta-evaluation dataset includes 2,988 human-annotated examples. |
TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA by Content Planning and Execution-based Reasoning (2024.acl-long)
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| Challenge: | Long-form table question answering often generates paragraph long and complex answers . a prevalent and concerning issue is hallucination, where models generate answers that are coherent yet factually incorrect or irrelevant to the input context. |
| Approach: | They propose a modular framework that decomposes the whole process into three sub-modules . framework produces a QA-based plan first, followed by generating an answer conditioned on this plan . human evaluation results indicate the framework improves strong baselines on accuracy and truthfulness . |
| Outcome: | The proposed framework improves accuracy and truthfulness on the FeTaQA and QTSumm datasets. |